This August we had three Capstone Showcase events: for the Master of Information and Cybersecurity (MICS) program, the Master of Information and Data Science (MIDS) program, and the 5th Year MIDS program.
Capstone projects are the culmination of the MICS and MIDS students’ work in their respective I School programs. Over the course of their final semester, teams of students propose and select project ideas, conduct and communicate their work, receive and provide feedback, and deliver compelling presentations along with a web-based final deliverable.
Congratulations to our winners, and to all the students who completed Capstone this summer!
Lily L. Chang MICS Capstone Award, August 2022
OZKi
Lauren Ayala, Suvojit Basak, Antony Halim, and Mariah Martinez
OZKi (Open Zero Knowledge Integration) is a zk-SNARK-based proving framework designed to help webapp developers implement privacy-protecting features with minimal effort. OZKi has two main components: the OZKi Toolkit and OZKi Oracle service which together provide a secure end-to-end proving system.
MICS Capstone Video Award
Kohana, by Sujith Dhati, Laura Haddad, Ismail Kably, Emma Rochon, and Nathaniel Singer, aims to show the advantages of adversary engagement through the use of deception technology.
Cal Cybersecurity Research Fellowship Award, 2022-23
Kohana has also been awarded the Center for Long-Term Cybersecurity’s 2022-23 Cal Cybersecurity Research Fellowship in support of their research on reducing the mean time to detect (MTTD) an adversarial data breach.
Hal R. Varian MIDS Capstone Award, August 2022
Metamaterial AI
Dante Malagrino, Parham Motameni, and Spencer Song
Metamaterials are lab-fabricated materials specifically engineered so that a property not found in naturally occurring materials can be exploited. Metamaterial AI leverages deep learning to solve this design problem and make it possible to quickly and easily identify the fabrication parameters to obtain the desired physical properties.
5th Year MIDS Capstone Award, 2022
Wildfire Rx
Joyce Li, Casey McGonigle, Jenna Morabito, Kaavya Shah, and Meer Wu
Wildfire RX uses satellite data to quantify California’s fire topography by building both a database of historical fire severity and a regression model predicting future fire severity. The tool helps fire managers choose which fires to suppress and which to let burn.
